Evaluation, classification and clustering with neuro-fuzzy techniques in integrate pest management

Abstract

In the present article are described the results obtained by the application of neuro-fuzzy methodologies in the study of Bactrocera Oleae (olive fly) infestation in Liguria region olive grows. The main aim of this project is create an informatic decisional support for experts in the applications of Integrated Pest Management strategies against the Bactrocera Oleae infestation. This system will suggest an appropriate treatments for each monitored farm to optimize the quality of the olive oil and the economic and environmental impact of these treatments. Forecast and statistical analyses on agronomic data sets like the case in study (the growth of olive fly), are actually made using standard approaches like analytical ones; this kind of data are very variable and non-linear, characteristics which make them complex to be treated mathematically. Agronomic research needs to introduce new analysis techniques for taking data and information, for example neuro-fuzzy techniques that allow a large use of infestation data with a good flexibility degree. © Springer-Verlag Berlin Heidelberg 2001.